ArticleeLife2020
Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 82 citations in OpenAlex.
- Decoding selective auditory attention to musical elements in ecologically valid music listening.Scientific reports · 2026Article
- Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex.Nature neuroscience · 2026Article
- Neurons in auditory cortex integrate information within a constrained and context-invariant temporal window.Current biology : CB · 2025Article
- Temporal integration in human auditory cortex is predominantly yoked to absolute time.Nature neuroscience · 2025Article
- Temporal recurrence as a general mechanism to explain neural responses in the auditory system.Communications biology · 2025Article
- Neural entrainment by speech in human auditory cortex revealed by intracranial recordings.Progress in neurobiology · 2025Article
- Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate.bioRxiv : the preprint server for biology · 2025Article
- Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes.Science advances · 2025Article
- Interference of Mid-level Speech and Noise Statistics Underlies Human Speech Recognition Sensitivity in Natural Environmental Noise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A hierarchy of processing complexity and timescales for natural sounds in the human auditory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Sparse high-dimensional decomposition of non-primary auditory cortical receptive fields.PLoS computational biology · 2025Article
- Modelling neural coding in the auditory midbrain with high resolution and accuracy.Nature machine intelligence · 2025Article
- Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina.PLoS computational biology · 2024Article
- Article
- Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.PLoS biology · 2023Article
- Dissecting neural computations in the human auditory pathway using deep neural networks for speech.Nature neuroscience · 2023Article
- Composite receptive fields in the mouse auditory cortex.The Journal of physiology · 2023Article
- Scalable Gaussian process inference of neural responses to natural images.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss.eLife · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.